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Evolutionary competition often occurs simultaneously at multiple levels of organization, in which traits or behaviors that are costly for an individual can provide collective benefits to groups to which the individual belongs. Building off of recent work that has used ideas from game theory to study evolutionary competition within and among groups, we study a PDE model for multilevel selection that considers group-level evolutionary dynamics through a pairwise conflict depending on the strategic composition of the competing groups. This model allows for incorporation of group-level frequency dependence, facilitating the exploration for how the form of probabilities for victory in a group-level conflict can impact the long-time support for cooperation via multilevel selection. We characterize well-posedness properties for measure-valued solutions of our PDE model and apply these properties to show that the population will converge to a delta-function at the all-defector equilibrium when between-group selection is sufficiently weak. We further provide necessary conditions for the existence of bounded steady state densities for the multilevel dynamics of Prisoners' Dilemma and Hawk-Dove scenarios, using a mix of analytical and numerical techniques to characterize the relative strength of between-group selection required to ensure the long-time survival of cooperation via multilevel selection. We also see that the average payoff at steady state appears to be limited by the average payoff of the all-cooperator group, even for games in which groups achieve maximal average payoff at intermediate levels of cooperation, generalizing behavior that has previously been observed in PDE models of multilevel selection with frequency-indepdent group-level competition., Comment: 60 pages. 12 figures

Future wireless connectivity is envisioned to accommodate functionalities far beyond broadband data transmission over point-to-point direct links, enabling novel scenarios, such as communication behind blockers and around corners, and innovative concepts, such as situational awareness, localization and joint communications and sensing. In this landscape, beams that are able to propagate on bent paths are ideal candidates for dynamic blockage avoidance, interference management in selected regions, and user connectivity on curved trajectories. In this work, we study beam shaping for applications in near-field wireless connectivity. We explain the underlying mechanism of beam bending and we present the design principles for tailoring the curvature of the propagation trajectory. We discuss design aspects for generation of such beams with large arrays and analyze the impact of several parameters on their performance, including the beam's footprint shape, the aperture size, the inter-element spacing, the sub-array selection of active elements, the available phase levels of the array elements and the operating frequency. We introduce the concept of near-field virtual routing (NFVR) and we demonstrate that such beams are able to address challenges of high frequency communications, such as dynamic routing, blockage avoidance and energy-efficiency, more efficiently than conventional beamforming., Comment: 14 pages, 15 figures

Localization is expected to play a significant role in future wireless networks as positioning and situational awareness, navigation and tracking, are integral parts of 6G usage scenarios. Nevertheless, in many cases localization requires extra equipment, which interferes with communications systems, while also requiring additional resources. On the other hand, high frequency and highly directional communications offer a new framework of improved resolution capabilities in the angular and range domains. The implementation of integrated sensing and communications is being explored to unify the sensing and communications systems and promote a communicate-to-sense approach. To this end, a localization algorithm is presented that utilizes beam-forming and the emerging beam-focusing technique, to estimate the location of the receiver. The algorithm can be implemented with large antenna arrays, and large intelligent surfaces. The performance of the algorithm for static and mobile users is evaluated through Monte-Carlo simulations. The results are presented with the empirical CDF for both static and mobile users, and the probability of successful estimation for static users., Comment: submitted for publication to IEEE Open Journal of Vehicular Technology

Spatial division multiple access (SDMA), a powerful method routinely applied in multi-user multiple-input multiple-output (MIMO) communications, relies on the angular orthogonality of beams in the far field, to distinguish multiple users at different angles. Yet, with the gradual shift of wireless connectivity to the near-field of large radiating apertures, the applicability of classical SDMA becomes questionable. Therefore, to enable near-field multiple access, it is necessary to design beams that have the desired orthogonality in the near-field. In this work, we propose the concept of near-field space division multiple access (NF-SDMA), to enable SDMA in the near-field. We demonstrate analytically that the orthogonality of beams is preserved at any location of the receiver, from the near-field to the far-field of the transmitter. By judicious design, we select the family of cosine beams and we prove that they satisfy the orthogonality condition, offering a multitude of communication modes in the near-field. We demonstrate how the correlation of beams generated with uniform linear arrays (ULAs) is extended to uniform planar arrays (UPAs) in a straightforward and insightful manner. To test our analytical findings, we propagate the designed beams numerically, and we measure their orthogonality both at the transmitter and the receiver. We verify that the orthogonality of the proposed beams is successfully retrieved at a receiver that resides in the near-field of the transmitter, and is also robust to displacements of the receiver. Based on our findings, we propose codebook designs for NF-SDMA that are applicable for receivers with many elements and even with single antennas., Comment: 12 pages, 9 figures

The design of Reconfigurable Intelligent Surfaces (RISs) is typically based on treating the RIS as an infinitely large surface that steers incident plane waves toward the desired direction. In practical implementations, however, the RIS has finite size and the incident wave is a beam of finite $k$-content, rather than a plane wave of $\delta$-like $k$-content. To understand the implications of the finite extent of both the RIS and the incident beam, here we treat the RIS as a spatial filter, the transfer function of which is determined by both the prescribed RIS operation and the shape of the RIS boundary. Following this approach, we study how the RIS transforms the incident $k$-content and we demonstrate how, by engineering the RIS shape, size, and response, it is possible to shape beams with nontrivial $k$-content to suppress unwanted interference, while concentrating the reflected power to desired directions. We also demonstrate how our framework, when applied in the context of near-field communications, provides the necessary insights into how the wavefront of the beam is tailored to enable focusing, propagation with invariant profile, and bending, beyond conventional beamforming., Comment: 13 pages, 9 figures

This paper introduces a novel reinforcement learning (RL) strategy designed to facilitate rapid autonomy transfer by utilizing pre-trained critic value functions from multiple environments. Unlike traditional methods that require extensive retraining or fine-tuning, our approach integrates existing knowledge, enabling an RL agent to adapt swiftly to new settings without requiring extensive computational resources. Our contributions include development of the Multi-Critic Actor-Critic (MCAC) algorithm, establishing its convergence, and empirical evidence demonstrating its efficacy. Our experimental results show that MCAC significantly outperforms the baseline actor-critic algorithm, achieving up to 22.76x faster autonomy transfer and higher reward accumulation. This advancement underscores the potential of leveraging accumulated knowledge for efficient adaptation in RL applications.

Wireless communications are nowadays shifting to higher operation frequencies with the aim to meet the ever-increasing demand for bandwidth. While reconfigurable intelligent surfaces (RISs) are usually envisioned to restore the line-of-sight of blocked links and to efficiently counteract the increased pathloss, their functionalities can extend far beyond these basic operations. Owing to their large surface and the multitude of scatterers, RISs can be exploited to perform advanced wavefront engineering, essentially transforming the incident beam into a non-trivial reflected beam that is able to address the challenges of high frequencies more efficiently than conventional beam-forming. In this paper it is demonstrated how advanced wavefront engineering with RISs enables beam profiles that are able to focus, bend and self-heal, thus offering functionalities beyond the current state-of-the-art. Their potential as enablers of perceptive, resilient, and efficient networks is discussed, and a localization technique based on a hybrid beam-forming/beam-focusing scheme is demonstrated., Comment: Submitted for publication in IEEE Vehicular Technology Magazine

A computationally-simplified and descriptor-richer Point Cloud Quality Assessment (PCQA) metric, namely PointPCA+, is proposed in this paper, which is an extension of PointPCA. PointPCA proposed a set of perceptually-relevant descriptors based on PCA decomposition that were applied to both the geometry and texture data of point clouds for full reference PCQA. PointPCA+ employs PCA only on the geometry data while enriching existing geometry and texture descriptors, that are computed more efficiently. Similarly to PointPCA, a total quality score is obtained through a learning-based fusion of individual predictions from geometry and texture descriptors that capture local shape and appearance properties, respectively. Before feature fusion, a feature selection module is introduced to choose the most effective features from a proposed super-set. Experimental results show that PointPCA+ achieves high predictive performance against subjective ground truth scores obtained from publicly available datasets. The code is available at \url{https://github.com/cwi-dis/pointpca_suite/}., Comment: ICIP 2023

In this paper, we investigate the physical layer security capabilities of reconfigurable intelligent surface (RIS) empowered wireless systems. In more detail, we consider a general system model, in which the links between the transmitter (TX) and the RIS as well as the links between the RIS and the legitimate receiver are modeled as mixture Gamma (MG) random variables (RVs). Moreover, the link between the TX and eavesdropper is also modeled as a MG RV. Building upon this system model, we derive the probability of zero-secrecy capacity as well as the probability of information leakage. Finally, we extract the average secrecy rate for both cases of TX having full and partial channel state information knowledge., Comment: 12 pages, 5 figures

Due to the non-ideality of analog components, transceivers experience high levels of hardware imperfections, like in-phase and quadrature imbalance (IQI), which manifests itself as the mismatches of amplitude and phase between the I and Q branches. Unless proper mitigated, IQI has an important and negative impact on the reliability and efficiency of high-frequency and high-data-rate systems, such as terahertz wireless networks. Recognizing this, the current paper presents an intelligent transmitter (TX) and an intelligent receiver (RX) architecture that by employing machine learning (ML) methodologies is capable to fully-mitigate the impact of IQI without performing IQI coefficients estimation. They key idea lies on co-training the TX mapper's and RX demapper in order to respectively design a constellation and detection scheme that takes accounts for IQI. Two training approaches are implemented, namely: i) conventional that requires a considerable amount of data for training, and ii) a reinforcement learning based one, which demands a shorter dataset in comparison to the former. The feasibility and efficiency of the proposed architecture and training approaches are validated through respective Monte Carlo simulations., Comment: 6 pages, 4 figures